In binary classification problems, samples can be treated as:
- True Positive (TP)
- False Positive (FP)
- True Negative (TN)
- False Negative (FN)
Especially, TP + FP + TN + FN = all samples.
In order to be more intuitive, we get a table:
| Predicted class | Predicted class | ||
| Class = 1 | Class = 0 | ||
| Actual class | Class = 1 | TP (11) | FN(10) |
| Actual class | Class = 0 | FP(01) | TN(00) |
Precision = TP / ( TP + FP)
Sensitivity / Recall = TP / (TP + FN)
Specificity = TN / (TN + FP)